A new class of similarity measures for robust image registration
Computer Vision, Graphics, and Image Processing
On the Sensitivity of the Hough Transform for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object recognition through invariant indexing
Object recognition through invariant indexing
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Grouping Principle and Four Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Images Similarity Detection Based on Directional Gradient Angular Histogram
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Comparison of Affine Region Detectors
International Journal of Computer Vision
SIAM Journal on Imaging Sciences
Hi-index | 0.00 |
A novel and general criterion for image similarity validation is introduced using the so-called a contrario decision framework. It is mathematically proved that it is possible to compute a fully automatic detection criterion to decide that two images have a common cause, which can be taken as a definition of similarity. Analytical estimates of the necessary and sufficient number of sample points are also given. An implementation of this criterion is designed exploiting the comparison of grey level gradient direction at randomly sampled points. Similar images are detected a contrario, by rejecting an hypothesis that resemblance is due to randomness, which is far more easy to model than a realistic degradation process. The method proves very robust to noise, transparency and partial occlusion. It is also invariant to contrast change and can accomodate global geometric transformations. It does not require any feature matching step. It can be global or local, only the global version is investigated in this paper.